Equipment damage prediction system

ABSTRACT

A generative adversarial network (GAN) system includes a generator sub-network configured to examine one or more images of actual damage to equipment. The generator sub-network also is configured to create one or more images of potential damage based on the one or more images of actual damage that were examined. The GAN system also includes a discriminator sub-network configured to examine the one or more images of potential damage to determine whether the one or more images of potential damage represent progression of the actual damage to the equipment.

FIELD

The subject matter described herein relates to image analysis systemsthat use one or more neural networks.

BACKGROUND

Equipment can become worn over time and, eventually, fail. For example,blades in turbines may develop spalls or cracks over time, which canlead to catastrophic failure of the turbines and/or significant downtimeof the turbines if the damage is not discovered sufficiently early toavoid significant repair or replacement of parts in the turbines. Someknown systems and methods can visually inspect the components ofequipment in order to identify damage to the equipment.

But, these systems and methods have certain faults. As one example, thecharacterization of the damage appearing in images or video of theequipment can be highly subjective and prone to error. As anotherexample, determination of the severity and/or likely spread of thedamage can require a significant amount of information on the materialsin the equipment, the environmental conditions to which the materialswere exposed, the operating conditions in which the equipment operated,etc., may need to be known to accurately identify, characterize, and/orpredict upcoming growth of the damage. This information may not beavailable for the automated analysis and/or prediction of upcominggrowth or changes in damage to the equipment.

BRIEF DESCRIPTION

In one embodiment, a GAN system includes a generator sub-networkconfigured to examine one or more images of actual damage to equipment.The generator sub-network also is configured to create one or moreimages of potential damage based on the one or more images of actualdamage that were examined. The GAN system also includes a discriminatorsub-network configured to examine the one or more images of potentialdamage to determine whether the one or more images of potential damagerepresent progression of the actual damage to the equipment.

In one embodiment, a method includes examining one or more images ofactual damage to equipment using a generator sub-network of a GAN,creating one or more images of potential damage using the generatorsub-network based on the one or more images of actual damage that wereexamined, and determining whether the one or more images of potentialdamage represent progression of the actual damage to the equipment byexamining the one or more images of potential damage using adiscriminator sub-network of the GAN.

In one embodiment, a GAN system includes a generator sub-networkconfigured to be trained using one or more images of actual damage toequipment. The generator sub-network also is configured to create one ormore images of potential damage based on the one or more images ofactual damage that were examined. The GAN system also includes adiscriminator sub-network configured to examine the one or more imagesof potential damage to determine whether the one or more images ofpotential damage represent progression of the actual damage to theequipment.

BRIEF DESCRIPTION OF THE DRAWINGS

The present inventive subject matter will be better understood fromreading the following description of non-limiting embodiments, withreference to the attached drawings, wherein below:

FIG. 1 illustrates one embodiment of an equipment damage predictionsystem;

FIG. 2 illustrates a flowchart of one embodiment of a method forpredicting progression of damage in equipment based on images;

FIG. 3 illustrates a generator sub-network of a generative adversarialnetwork (GAN) system shown in FIG. 1 creating a batch of images ofpotential damage to equipment; and

FIG. 4 illustrates one example of a repair system that uses predicteddamage progression as determined by a GAN system.

DETAILED DESCRIPTION

One or more embodiments of the inventive subject matter described hereinprovide systems and methods that predict the growth or progression ofdamage to equipment. The systems and methods can predict the progressionof damage to the equipment based on or using one or more images ofactual damage to the same or other equipment, as well as one or moreimages generated by a neural network based on the image(s) of actualdamage. For example, a deep generative adversarial network (GAN) systemcan use a large image dataset without annotation (e.g., labeling of whateach pixel in the images represents) to generate high-quality images.The GAN system can be conditioned using images representative of thecurrent stage or state of the equipment (e.g., a turbine engine blade)and be trained with images including possible developed defects to theequipment. A generator sub-network of the GAN system can create theimages of possible or potential growth of the damage based on theimage(s) of actual damage. The created images can be provided to adiscriminator sub-network of the GAN system. The discriminatorsub-network determines a loss function, error, and/or confidence valueindicative of whether the image created by the generator sub-network isor likely is actual damage to the equipment. If the loss function,error, and/or confidence value indicate that the image created by thegenerator sub-network is similar to the image of actual damage to theequipment (e.g., the loss function or error is lower than a lowerdesignated threshold, or the confidence value is at least as great as anupper designated threshold), then the created image can be used tocharacterize how damage to equipment changes over time.

Different created images can represent potential progression orworsening of damage to the equipment. For example, different createdimages can be associated with different degrees or stages of damageprogression. Subsequent images of the same or different equipment can becompared to the created image(s). If the damage appearing in thesubsequent images is similar to the potential damage appearing in thecreated image(s), then a determination may be made as to how quickly thedamage to the equipment is progressing. This determination can be usedto determine whether to modify a maintenance schedule of the equipmentand, optionally, to automatically implement or perform repair orreplacement of the equipment by an automated system (e.g., an automatedrobotic or other powered system).

The GAN system described above includes two sub-networks, namely thegenerator sub-network and the discriminator sub-network. Thesesub-networks interact in a setting of a two-player minimax game. Duringtraining, the generator sub-network attempts to learn how to producereal-looking image samples (e.g., created images) based on trainingimages (e.g., images of actual damage) provided to the generatorsub-network. The discriminator sub-network attempts to learn how todistinguish the produced image samples from the genuine (e.g., training)image samples, which are original and not produced by the generatorsub-network. These sub-networks can eventually converge to anequilibrium point where the generator sub-network produces image sampleswhich are indistinguishable (from the perspective of the discriminatorsub-network) from the genuine image samples.

At least one technical effect of the systems and methods describedherein includes the prediction of how damage to equipment will progressor worsen. This prediction can be used to determine whether and/or whento repair the equipment and, in one embodiment, can be used toautomatically repair the equipment from a damaged state to a repairedstate.

FIG. 1 illustrates one embodiment of an equipment damage predictionsystem 100. FIG. 2 illustrates a flowchart of one embodiment of a method200 for predicting equipment damage progression. The flowchart of themethod 200 can represent operations performed by the system 100 shown inFIG. 1, such as functions performed by one or more processors (e.g., oneor more microprocessors, field programmable gate arrays, and/orintegrated circuits) under the direction of software, to determine orpredict optical flow from images. Optionally, the flowchart of themethod 200 can represent an algorithm used to create (e.g., write) suchsoftware.

The system 100 includes neural networks 102, 104 and, in one embodiment,represents a GAN system. The neural networks 102, 104 are artificialneural networks formed from one or more processors (e.g.,microprocessors, integrated circuits, field programmable gate arrays, orthe like). The neural networks 102, 104 are divided into two or morelayers 106, such as input layers that receive images, output layers thatoutput an image or loss function (e.g., error, as described below), andone or more intermediate layers. The layers 106 of the neural networks102, 104 represent different groups or sets of artificial neurons, whichcan represent different functions performed by the processors on theimages to predict how damage to equipment will progress over time. Theneural network 102 represents a generator sub-network of a GAN, and theneural network 104 represents a discriminator sub-network of the GAN inone embodiment.

In operation, at 202 in the flowchart of the method 200 shown in FIG. 2,the generator sub-network 102 receives images 108 of actual damage 112to equipment. The equipment (or a component of equipment) can include asurface of a turbine, such as a surface of a turbine blade, nozzle, orthe like. Optionally, the equipment can include other components, suchas the surface of a road or sidewalk, a surface of a vehicle, or thelike, that may be damaged over time. The damage 112 can representspalling, cracks, rust, pitting, or the like, in the equipment. Theimages 108 of actual damage 112 can be obtained by one or more camerasgenerating the images 108 based on equipment that already has the actualdamage 112.

The image(s) 108 can be obtained by the generator sub-network 102 by acamera communicating the image(s) 108 to the generator sub-network 102via one or more wired and/or wireless connections. Optionally, theimage(s) 108 can be stored in a tangible and non-transitory computerreadable memory, such as a computer hard drive, optical disk, or thelike, and be accessible by the generator sub-network 102 via one or morewired and/or wireless connections.

At 204 in the method 200, the generator sub-network 102 is trained usingthe image(s) 108 of actual damage 112. The processors of the generatorsub-network 102 can examine characteristics of pixels 114 in theimage(s) 108 of actual damage 112. These characteristics can includelocations of the pixels 114 in the image(s) 108, intensities of thepixels 114, colors of the pixels 114, etc. The generator sub-network 102can determine statistical distributions (e.g., Gaussian distributions)of the pixel characteristics. Different distributions can be determinedfor different pixels or locations in the image(s) 108. The generatorsub-network 102 can examine the statistical distributions and determineprobabilities of each pixel 114 having various characteristics.

At 206, one or more images 110 of potential damage 116 to the equipmentare generated by the generator sub-network 102. The generatorsub-network 102 can generate the created image(s) 110 based on thedistributions and probabilities of pixel characteristics that weredetermined at 204. The generator sub-network 102 creates one or moredistribution-based images 110 that are predictions of what other damage116 to the equipment could look like based on the characteristics of thepixels 114 in the image(s) 108 of actual damage 112 to the equipment.The generator sub-network 102 can create multiple created images 110 ofdifferent types of potential damage 116 that could occur to theequipment based on the input image(s) 108 of actual damage 112.

FIG. 3 illustrates the generator sub-network 102 of the system 100 shownin FIG. 1 creating a batch 306 of images 300, 302, 304 of potentialdamage 116, 308, 310 to the equipment. The generator sub-network 102 cancreate multiple distribution-based images 300, 302, 304 based on thecharacteristics of the images 108 of actual damage 112 that wereobtained by or otherwise provided to the generator sub-network 102. Asshown in FIG. 3, some of the created images 300, 302, 304 are morelikely to represent progression of the damage 112 to the equipment thanother created images 300, 302, 304.

For example, the predicted damage 116 appearing in the created image 300shows growth of the actual damage 112 so that a convex portion of theactual damage 112 is filled with the predicted damage 116. This may be alikely progression of the actual damage 112. As another example, thepredicted damage 310 appearing in the created image 304 shows growth ofthe actual damage 112 along all or substantially all (e.g., at least75%) of the outer perimeter of the actual damage 112. This also may be alikely progression of the actual damage 112. But, the predicted thepredicted damage 308 appearing in the created image 302 shows growth ofthe actual damage 112 along a singular, narrow extension. This also maynot be a likely progression of the actual damage 112.

At 208 in the method 200 shown in FIG. 2, the created image(s) 110, 300,302, 304 are examined by the discriminator sub-network 104 of the GANsystem 100. In one embodiment, the discriminator sub-network 104determines loss functions or errors for the created images 110, 300,302, 304. The loss functions or errors can represent a confidence thatthe potential damage 116, 308, 310 appearing in the created images 110,300, 302, 304 is likely to occur or develop from the damage 112 shown inthe image 108. For example, large loss functions or errors can indicatethat the potential damage 116, 308, 310 is less likely to develop fromthe damage 112 than smaller loss functions or errors.

The discriminator sub-network 104 can determine the loss function,error, and/or confidence value by examining characteristics of thepixels 114 in the created images 110, 300, 302, 304. For example, thediscriminator sub-network 104 can determine that the characteristic of afirst pixel 114 in a created image 110, 300, 302, 304 is more similar tothe distribution of pixel characteristics associated with actual images108 of damage 112 than a different, second pixel 114 in the createdimage 110, 300, 302, 304. The first pixel 114 can be associated (by thediscriminator sub-network 104) with a greater confidence value (orsmaller loss function or error) than the second pixel 114. Theconfidence values, loss functions, and/or errors can be determined formany or all pixels 114 in a created image 110, 300, 302, 304. Createdimages 110, 300, 302, 304 having pixels 114 with larger confidencevalues, smaller loss functions, or smaller errors can be determined bythe discriminator sub-network 104 to depict actual or likely progressionof damage to the component shown in the image 110, 300, 302, 304 thancreated images 110, 300, 302, 304 having smaller confidence values,larger loss functions, or larger errors.

In one embodiment, the artificial neurons in the layers 106 of thediscriminator sub-network 104 can examine individual pixels 114 in thecreated images 110, 300, 302, 304. The processors (operating as theartificial neurons) can use linear classification to calculate scoresfor different categories of objects (referred to herein as “classes”),such as a tree, a car, a person, a bird, spalling of a thermal barriercoating, a crack in a surface, a sign, or the like. These scores canindicate the probability that a pixel 114 represents different classes.Each artificial neuron can apply a mathematical function, such as anactivation function, to the same pixel, with the functions applied bydifferent neurons impacting the functions applied by other neurons anddifferent neurons applying different weights to different terms in thefunctions than one or more, or all other neurons. Application of thefunctions generates the classification scores for the pixels 114, whichcan be used to identify the objects in the images 110, 300, 302, 304.The neurons in the layers 106 of the discriminator sub-network 104examine the characteristics of the pixels 114, such as the intensities,colors, or the like, to determine the scores for the various pixels 114.

For example, the discriminator sub-network 104 can determine that afirst pixel 114 in one of the created images 110, 300, 302, 304 has ascore vector of [0.6 0.15 0.05 0.2]. This score vector indicates thatthe discriminator sub-network 104 has calculated a 60% probability thatthe first pixel 114 represents a first object class (e.g., a human bodyor person), a 15% probability that the first pixel 114 represents asecond object class (e.g., a car), a 5% probability that the first pixel114 represents a third object class (e.g., a tree), and a 20%probability that the first pixel 114 represents a fourth object class(e.g., the ground). This process can be repeated for several, or all,other pixels 114 in the same image 110, 300, 302, 304.

The processors of the discriminator sub-network 104 can then determinethe loss functions or errors for the pixels 114 in the images 110, 300,302, 304. The loss function or error can be calculated as a differencebetween a selected object class for a pixel 114 and the object score forthat object class. This error value can be a difference between 100% (orone) and the probability of the selected object class. With respect tothe preceding example, the first object class is the selected objectclass for the pixel 114 because the first object class has a largerprobability (i.e., 60%) than the other object classes for that samepixel 114. The loss function or error for that pixel 114 can becalculated as [0.4 −0.15 −0.05 −0.2]. The value of 0.4 (or 40%) iscalculated as the difference between one and 0.6 (or between 100% and60%). This process can be repeated for several, or all, of the pixels114.

At 210 in the method 200, a determination is made as to whether thediscriminator sub-network 104 identifies the created image 110, 300,302, 304 as an image of actual damage to the component. For example, adetermination is made as to whether the generator sub-network 102 wasable to create an image of potential damage that was determined by thediscriminator sub-network 104 to be an actual image of actual damage.The discriminator sub-network 104 can examine the loss functions of thecreated images 110, 300, 302, 304, compare the loss functions of thecreated images 110, 300, 302, 304 to each other, compare the lossfunctions of the created images 110, 300, 302, 304 to thresholds, or thelike, to determine which, if any, of the created images 110, 300, 302,304 appears to show actual damage to the component. The discriminatorsub-network 104 can determine that a created image 110, 300, 302, 304does not depict damage to a component responsive to the loss functionsassociated with the created image 110, 300, 302, 304 indicating largererror (e.g., relative to a designated threshold). The discriminatorsub-network 104 can determine that the potential damage appearing in acreated image 110, 300, 302, 304 is similar to actual damage appearingin one or more images 108 responsive to the loss functions associatedwith the distribution-based image indicating a smaller error (e.g.,relative to a designated threshold).

If the discriminator sub-network 104 determines that the potentialdamage appearing in a created image 110, 300, 302, or 304 is similar tothe actual damage appearing in the original image 108 (e.g., the erroris not significant), then the potential damage appearing in the image110, 300, 302, or 304 created by the generator sub-network 102 is alikely growth or progression of the actual damage. As a result, flow ofthe method 200 can proceed toward 212. But, if the discriminatorsub-network 104 determines that the potential damage appearing in thecreated image 110, 300, 302, or 304 is not similar to the actual damageappearing in the original image 108 (e.g., the error is significant),then the potential damage appearing in the created image 110, 300, 302,or 304 is not an accurate prediction of the progression of damage. As aresult, flow of the method 200 can return toward 206. For example, themethod 200 can return to creating one or more additional images 110,300, 302, 304 showing other or different potential damage forexamination by the discriminator sub-network 104. Optionally, the method200 can terminate.

At 212, the image of potential damage is used in monitoring and/orrepairing components. For example, additional images of actual damage tothe same or other components can be compared with images of potentialdamage. This comparison can be performed manually or can be performedautomatically (e.g., using the discriminator sub-network 104). The imageof potential damage (identified at 210) can be used to determine how theactual damage is likely to change over time. If the growth orprogression of the damage is sufficiently severe (e.g., the damagegrowth is larger than a designated threshold), one or more responsiveactions can be implemented. For example, an automated system (e.g., arobotic system) can automatically repair the damaged portion of thecomponent, such as by spraying an additive onto a thermal barriercoating on a turbine blade having the damage. As another example, amaintenance schedule of the component can be changed to provide forrepair or maintenance sooner (in situations where the likely progressionof the damage is more significant) or later (in situations where thelikely progression of the damage is less significant).

FIG. 4 illustrates one example of a repair system 400 that usespredicted damage progression as determined by a GAN system 402. The GANsystem 402 represents one or more embodiments of the system 100described above. The repair system 400 includes a sensor 404 thatobtains images 108 of actual damage 112 to a component (e.g., an engine,turbine, turbine blade, exterior surface of an object, etc.) for the GANsystem 402. For example, the sensor 404 can be a camera that providesimages or video frames to the GAN system 402 as the images 108.Optionally, the control system 400 includes a memory 406, such as acomputer hard drive, optical disc, or the like, that stores the images108 for the GAN system 402.

The GAN system 402 can predict progression of the actual damage 112 tothe component as described above. The predicted damage progression canbe communicated to a controller 408 of an automated powered system 410.The controller 408 represents hardware circuitry that includes and/or isconnected with one or more processors (e.g., one or moremicroprocessors, field programmable gate arrays, integrated circuits,etc.). The controller 408 controls operation of the powered system 410,which can represent an automated robotic system that operates to repairthe component, such as by spraying an additive onto a coating of thecomponent, by replacing the component, or the like. The controller 408can examine the predicted damage progression and determine whether oneor more responsive actions need to be implemented. For example, if thepredicted progression of damage indicates that the component needs to berepaired or replaced, the controller 408 can generate and communicationa control signal to an actuator 412 of the powered system 410 thatautomatically sprays an additive onto a coating of the component, thatremoves the component, that replaces the component, etc. The actuator412 can include a spray device, a grasping hand of the powered system410, or the like.

In one embodiment, a GAN system includes a generator sub-networkconfigured to examine one or more images of actual damage to equipment.The generator sub-network also is configured to create one or moreimages of potential damage based on the one or more images of actualdamage that were examined. The GAN system also includes a discriminatorsub-network configured to examine the one or more images of potentialdamage to determine whether the one or more images of potential damagerepresent progression of the actual damage to the equipment.

Optionally, the discriminator sub-network is configured to determine oneor more loss functions indicative of errors in the one or more images ofpotential damage.

Optionally, the generator sub-network is configured to be trained usingthe one or more images of actual damage.

Optionally, the generator sub-network is configured to be trained usingthe one or more images of actual damage by determining distributions ofpixel characteristics of the one or more images of actual damage.

Optionally, the GAN system also includes a controller configured toimplement one or more actions responsive to determining that the one ormore images of potential damage represent progression of the actualdamage.

Optionally, the discriminator sub-network is configured determinewhether the one or more images of potential damage represent theprogression of the actual damage to the equipment by determining one ormore loss functions of the one or more images of potential damage.

Optionally, the discriminator sub-network is configured determine thatthe one or more images of potential damage represent the progression ofthe actual damage to the equipment responsive to the one or more lossfunctions of the one or more images of potential damage not exceeding adesignated threshold.

In one embodiment, a method includes examining one or more images ofactual damage to equipment using a generator sub-network of a GAN,creating one or more images of potential damage using the generatorsub-network based on the one or more images of actual damage that wereexamined, and determining whether the one or more images of potentialdamage represent progression of the actual damage to the equipment byexamining the one or more images of potential damage using adiscriminator sub-network of the GAN.

Optionally, the method also includes determining one or more lossfunctions indicative of errors in the one or more images of potentialdamage using the discriminator sub-network.

Optionally, the method also includes training the generator sub-networkusing the one or more images of actual damage.

Optionally, training the generator sub-network includes determiningdistributions of pixel characteristics of the one or more images ofactual damage.

Optionally, the method also includes implementing one or more actionsresponsive to determining that the one or more images of potentialdamage represent progression of the actual damage.

Optionally, determining whether the one or more images of potentialdamage represent the progression of the actual damage to the equipmentincludes determining one or more loss functions of the one or moreimages of potential damage.

Optionally, determining that the one or more images of potential damagerepresent the progression of the actual damage to the equipment occursresponsive to the one or more loss functions of the one or more imagesof potential damage not exceeding a designated threshold.

In one embodiment, a GAN system includes a generator sub-networkconfigured to be trained using one or more images of actual damage toequipment. The generator sub-network also is configured to create one ormore images of potential damage based on the one or more images ofactual damage that were examined. The GAN system also includes adiscriminator sub-network configured to examine the one or more imagesof potential damage to determine whether the one or more images ofpotential damage represent progression of the actual damage to theequipment.

Optionally, the discriminator sub-network is configured to determine oneor more loss functions indicative of errors in the one or more images ofpotential damage.

Optionally, the generator sub-network is configured to be trained usingthe one or more images of actual damage by determining distributions ofpixel characteristics of the one or more images of actual damage.

Optionally, the system includes a controller configured to implement oneor more actions responsive to determining that the one or more images ofpotential damage represent progression of the actual damage.

Optionally, the discriminator sub-network is configured determinewhether the one or more images of potential damage represent theprogression of the actual damage to the equipment by determining one ormore loss functions of the one or more images of potential damage.

Optionally, the discriminator sub-network is configured determine thatthe one or more images of potential damage represent the progression ofthe actual damage to the equipment responsive to the one or more lossfunctions of the one or more images of potential damage not exceeding adesignated threshold.

As used herein, an element or step recited in the singular and proceededwith the word “a” or “an” should be understood as not excluding pluralof said elements or steps, unless such exclusion is explicitly stated.Furthermore, references to “one embodiment” of the presently describedsubject matter are not intended to be interpreted as excluding theexistence of additional embodiments that also incorporate the recitedfeatures. Moreover, unless explicitly stated to the contrary,embodiments “comprising” or “having” an element or a plurality ofelements having a particular property may include additional suchelements not having that property.

It is to be understood that the above description is intended to beillustrative, and not restrictive. For example, the above-describedembodiments (and/or aspects thereof) may be used in combination witheach other. In addition, many modifications may be made to adapt aparticular situation or material to the teachings of the subject matterset forth herein without departing from its scope. While the dimensionsand types of materials described herein are intended to define theparameters of the disclosed subject matter, they are by no meanslimiting and are exemplary embodiments. Many other embodiments will beapparent to those of skill in the art upon reviewing the abovedescription. The scope of the subject matter described herein should,therefore, be determined with reference to the appended claims, alongwith the full scope of equivalents to which such claims are entitled. Inthe appended claims, the terms “including” and “in which” are used asthe plain-English equivalents of the respective terms “comprising” and“wherein.” Moreover, in the following claims, the terms “first,”“second,” and “third,” etc. are used merely as labels, and are notintended to impose numerical requirements on their objects. Further, thelimitations of the following claims are not written inmeans-plus-function format and are not intended to be interpreted basedon 35 U.S.C. § 112(f), unless and until such claim limitations expresslyuse the phrase “means for” followed by a statement of function void offurther structure.

This written description uses examples to disclose several embodimentsof the subject matter set forth herein, including the best mode, andalso to enable a person of ordinary skill in the art to practice theembodiments of disclosed subject matter, including making and using thedevices or systems and performing the methods. The patentable scope ofthe subject matter described herein is defined by the claims, and mayinclude other examples that occur to those of ordinary skill in the art.Such other examples are intended to be within the scope of the claims ifthey have structural elements that do not differ from the literallanguage of the claims, or if they include equivalent structuralelements with insubstantial differences from the literal languages ofthe claims.

What is claimed is:
 1. A generative adversarial network (GAN) systemcomprising: a generator sub-network configured to examine one or moreimages of actual damage to equipment, the generator sub-network alsoconfigured to create one or more images of potential damage based on theone or more images of actual damage that were examined; and adiscriminator sub-network configured to examine the one or more imagesof potential damage to determine whether the one or more images ofpotential damage represent progression of the actual damage to theequipment.
 2. The system of claim 1, wherein the discriminatorsub-network is configured to determine one or more loss functionsindicative of errors in the one or more images of potential damage. 3.The system of claim 1, wherein the generator sub-network is configuredto be trained using the one or more images of actual damage.
 4. Thesystem of claim 3, wherein the generator sub-network is configured to betrained using the one or more images of actual damage by determiningdistributions of pixel characteristics of the one or more images ofactual damage.
 5. The system of claim 1, further comprising a controllerconfigured to implement one or more actions responsive to determiningthat the one or more images of potential damage represent progression ofthe actual damage.
 6. The system of claim 1, wherein the discriminatorsub-network is configured determine whether the one or more images ofpotential damage represent the progression of the actual damage to theequipment by determining one or more loss functions of the one or moreimages of potential damage.
 7. The system of claim 6, wherein thediscriminator sub-network is configured determine that the one or moreimages of potential damage represent the progression of the actualdamage to the equipment responsive to the one or more loss functions ofthe one or more images of potential damage not exceeding a designatedthreshold.
 8. A method comprising: examining one or more images ofactual damage to equipment using a generator sub-network of a generativeadversarial network (GAN); creating one or more images of potentialdamage using the generator sub-network based on the one or more imagesof actual damage that were examined; and determining whether the one ormore images of potential damage represent progression of the actualdamage to the equipment by examining the one or more images of potentialdamage using a discriminator sub-network of the GAN.
 9. The method ofclaim 8, further comprising determining one or more loss functionsindicative of errors in the one or more images of potential damage usingthe discriminator sub-network.
 10. The method of claim 9, furthercomprising training the generator sub-network using the one or moreimages of actual damage.
 11. The method of claim 10, wherein trainingthe generator sub-network includes determining distributions of pixelcharacteristics of the one or more images of actual damage.
 12. Themethod of claim 8, further comprising implementing one or more actionsresponsive to determining that the one or more images of potentialdamage represent progression of the actual damage.
 13. The method ofclaim 8, wherein determining whether the one or more images of potentialdamage represent the progression of the actual damage to the equipmentincludes determining one or more loss functions of the one or moreimages of potential damage.
 14. The method of claim 13, whereindetermining that the one or more images of potential damage representthe progression of the actual damage to the equipment occurs responsiveto the one or more loss functions of the one or more images of potentialdamage not exceeding a designated threshold.
 15. A generativeadversarial network (GAN) system comprising: a generator sub-networkconfigured to be trained using one or more images of actual damage toequipment, the generator sub-network also configured to create one ormore images of potential damage based on the one or more images ofactual damage that were examined; and a discriminator sub-networkconfigured to examine the one or more images of potential damage todetermine whether the one or more images of potential damage representprogression of the actual damage to the equipment.
 16. The system ofclaim 15, wherein the discriminator sub-network is configured todetermine one or more loss functions indicative of errors in the one ormore images of potential damage.
 17. The system of claim 15, wherein thegenerator sub-network is configured to be trained using the one or moreimages of actual damage by determining distributions of pixelcharacteristics of the one or more images of actual damage.
 18. Thesystem of claim 15, further comprising a controller configured toimplement one or more actions responsive to determining that the one ormore images of potential damage represent progression of the actualdamage.
 19. The system of claim 15, wherein the discriminatorsub-network is configured determine whether the one or more images ofpotential damage represent the progression of the actual damage to theequipment by determining one or more loss functions of the one or moreimages of potential damage.
 20. The system of claim 19, wherein thediscriminator sub-network is configured determine that the one or moreimages of potential damage represent the progression of the actualdamage to the equipment responsive to the one or more loss functions ofthe one or more images of potential damage not exceeding a designatedthreshold.